Wireless communication systems are widely known in which base stations (also known as evolved Node Bs (eNBs)) communicate with mobile devices (also known as user equipments (UEs)) which are within range of the eNB. Each eNB divides its available bandwidth, i.e. frequency and time resources, into different resource allocations for the different UEs. There is a constant need to increase the capacity of such systems, and to improve the efficiency of resource utilisation, in order to accommodate more users (more UEs), more data-intensive services and/or higher data transmission rates.
OFDM (Orthogonal Frequency Division Multiplexing) is one technique used for transmitting data in wireless communication systems. An OFDM-based communications scheme divides data symbols to be transmitted among a large number of subcarriers; hence the term “frequency division multiplexing”. Data is modulated onto a subcarrier by adjusting its phase, amplitude, or both phase and amplitude. The “orthogonal” part of the name OFDM refers to the fact that the spacings of the subcarriers in the frequency domain are chosen so as to be orthogonal, in a mathematical sense, to the other subcarriers. In other words, they are arranged in the frequency domain such that the sidebands of adjacent subcarriers may overlap but such that inter-subcarrier interference is sufficiently minimised for the subcarriers to be received.
When individual subcarriers or sets of subcarriers are assigned to different users (different UEs), the result is a multi-access system referred to as OFDMA (Orthogonal Frequency Division Multiple Access). The term OFDM is often intended to include OFDMA. The two terms may therefore be considered interchangeable for the purposes of the present explanation. By assigning distinct frequency/time resources to each UE in a cell, OFDMA can help to avoid interference among UEs within a given cell.
A further modification of the basic OFDM scheme is called MIMO which stands for “multiple-input multiple-output”. This type of scheme employs multiple antennae at the transmitter and/or at the receiver (often at both) to enhance the data capacity achievable between the transmitter and the receiver. Typically, this is used to achieve enhanced data capacity between an eNB and the user equipment(s) (UE(s)) served by that eNB.
By way of example, a 2×2 “single user MIMO” (SU-MIMO) configuration contains two antennae at the transmitter and two antennae at a single receiver that is in communication with the transmitter. Likewise, a 4×4 SU-MIMO configuration contains four antennae at the transmitter and four antennae at the single receiver that is in communication with the transmitter. There is no need for the transmitter and receiver to employ the same number of antennae. Typically, an eNB in a wireless communication system will be equipped with more antennae in comparison with a UE, owing to differences in power, cost and size limitations. It should also be noted that so called “multi-user MIMO” (MU-MIMO) is often employed, and this involves a single eNB which is able to perform MIMO communication with multiple UEs at once. This is discussed further below.
The term “channel” is commonly used to refer to the frequency (or equivalently time delay) response of the radio link between a transmitter and a receiver. The MIMO channel (hereafter simply the “channel”) contains all the subcarriers (see the discussion on subcarriers above), and covers the whole bandwidth of transmission. A MIMO channel contains many individual radio links. The number of these individual radio links, which may each be individually referred to as a single-input single-output (SISO) channel, is NRX×NTX, where NTX is the number of antennae at the transmitter and NRX is the number of antennae at the receiver(s). For example, a 3×2 SU-MIMO arrangement contains 6 links, hence it has 6 SISO channels.
Considering the simplified 2×3 SU-MIMO system schematically represented in FIG. 1, it can be seen that antenna R0 of receiver R receives transmissions from each of the transmitter antennae T0, T1 and T2 of transmitter T. Similarly, receiver antenna R1 receives transmissions from transmitter antennae T0, T1 and T2. Therefore, the signal received at the receiver comprises (or is made up of) a combination of the transmissions (i.e. a combination of the six SISO channels) from the transmitter antennae. In general, SISO channels can be combined in various ways to transmit one or more data streams to the receiver.
FIG. 2 is a conceptual diagram of a more generalized SU-MIMO system. In FIG. 2, a transmitter transmits signals utilizing NTX transmitting antennae, and a single receiver receives the signals from the transmitter utilizing NRX receiving antennae. In order to create a mathematical model of the characteristics of the overall MIMO channel (in this case a SU-MIMO channel), it is necessary to represent the individual SISO channels between the transmitter and receiver. As shown in FIG. 2, the individual SISO channels are represented by H0,0 to HNRX-2NTX-2, and as suggested in the Figure, these form terms of a matrix commonly called the “channel matrix” or channel response matrix H. It will be recognised that H0,0 represents the channel characteristics (for example, channel frequency response) for transmitting signals from transmitting antenna 0 to receiving antenna 0. Similarly, “HNRX-2NTX-2” represents the channel characteristics for transmitting signals from the transmitting antenna NTX-1 to the receiving antenna NRX-1, and so on.
In FIG. 2, the symbols x0 to xNTX-2, which represent the signal elements transmitted using the transmitting antennae 0 to NTX-1 respectively, together form a transmitted signal vector x=(x0, x1, . . . , xNTX-2)T, where ( )T indicates the vector transpose. (In other words, x is the signal transmitted from the transmitter.) Likewise, the received signals elements y0 to yNRX-2 received by receiving antennae 0 to NRX-1 respectively, together form received signal vector y=(y0, y1, . . . , yNRX-2)T. (In other words, y is the signal received at the receiver.) The relationship between the vectors y and x for the simplified single user system shown in FIG. 2 may be modelled by the basic SU-MIMO system equation:y=Hx+n   (Equation 0)
where H is the channel matrix referred to above and n is a vector representing noise (usually assumed to be additive white Gaussian noise).
It should be noted at this point that FIG. 1 and FIG. 2 (discussed above) both relate to “single user” MIMO (SU-MIMO) systems. However, as also mentioned above, so called “multi-user” MIMO (MU-MIMO) is often employed, and this involves a single eNB which has multiple antennas and which is able to perform MIMO communication with multiple UEs (each of which may also have multiple antennas) at once. A schematic representation of a MU-MIMO system is given in FIG. 3.
More specifically, FIG. 3 shows a general MU-MIMO system where the eNB transmits data to different UEs on the same time-frequency from multiple transmit antennas. To minimise interference between UEs, the eNB creates transmission beams through precoding.
According to Wikipedia for example, “precoding” is a generalization of “beamforming” and is used to support multi-stream transmission in multi-antenna wireless communications. In conventional single-stream beamforming, the same signal is emitted from each of the transmit antennas with appropriate weighting (phase and gain) such that the signal power is maximized at the receiver. When the receiver has multiple antennas, however, single-stream beamforming cannot simultaneously maximize the signal level at all of the receive antennas. In order to maximize the throughput in multiple receive antenna systems, multi-stream transmission is generally required.
In multi-user MIMO (MU-MIMO), a multi-antenna transmitter communicates simultaneously with multiple receivers (each having one or multiple antennas), as explained above. From an implementation perspective, precoding algorithms for MU-MIMO systems fall into linear and nonlinear precoding types. The capacity achieving algorithms are generally nonlinear, but linear precoding approaches may still achieve reasonable performance with much lower complexity. Linear precoding strategies include, for example, maximum ratio transmission (MRT), zero-forcing (ZF) precoding, and transmit Wiener precoding.
While performance maximization has a clear interpretation in point-to-point SU-MIMO, a multi-user system generally cannot simultaneously maximize the performance for all users. Multi-user systems may therefore be said to involve a multi-objective optimization problem where each objective corresponds to maximization of the capacity of one of the users. One common way of addressing this problem is to select a system utility function; for example, the weighted sum capacity where the weights correspond to the system's subjective user priorities.
In any case, at the receiving side, a UE uses postcoding (decoding) to obtain its data from the received signal.
Those skilled in the art will be appreciated from the discussion above that precoding is often highly dependent on the state of the channel (i.e. it is dependent on the “channel state”)—see below.
Mathematically, a MU-MIMO system can be described (modelled) by modifying the simplified single user MIMO system equation (Equation 0) above as follows:
                              y          ⁡                      (            i            )                          =                                            H              ⁡                              (                i                )                                      ⁢                          V              ⁡                              (                i                )                                      ⁢                          x              ⁡                              (                i                )                                              +                                    ∑                                                k                  =                  1                                ,                                  k                  ≠                  i                                                            N                UE                                      ⁢                                                  ⁢                                          H                ⁡                                  (                  i                  )                                            ⁢                              V                ⁡                                  (                  k                  )                                            ⁢                              x                ⁡                                  (                  k                  )                                                              +                      n            ⁡                          (              i              )                                                          (                  Equation          ⁢                                          ⁢          1                )            
In Equation 1 above:                y(i) is the received signal at the i-th UE,        x(i) is the data signal for the i-th UE,        H(i) is the channel matrix for the i-th UE,        V(i) is the precoder matrix of the i-th UE,        n(i) is the additive white Gaussian noise at the i-th user.        
MIMO transmission schemes may be said to be either “non-adaptive” or “adaptive”. In the non-adaptive case, the transmitter does not have any knowledge of the condition or properties of the channel. In other words, the transmitter does not have any knowledge of the way a transmitted signal changes as it is transmitted “through the air”. This lack of knowledge regarding the “channel state” can limit performance as the transmitter cannot take account of, for example, changes in conditions which cause changes in the state or properties of the channel (which affect how a transmitted signal changes “in the air”). Adaptive schemes rely on the feedback of information (so-called “channel-state information” or CSI) from the receiver to the transmitter (i.e. in the uplink (UL)), which allows modification of transmitted downlink (DL) signals to account for changing conditions (i.e. to account for the changing channel state) and to maximise data throughput. In other words, the feedback of CSI can be used to facilitate or assist with precoding. The present invention is concerned primarily with these adaptive types of MIMO schemes. The feedback of CSI in the uplink, from different UEs, is illustrated in FIG. 4.
The following table contains certain abbreviations/acronyms that may be found herein:
CSIchannel state information (includes PMI, RI and CQI)CQIchannel quality indicatorDLdownlinkeNBevolved Node B (base station)MIMOmultiple-input multiple-outputMU-MIMOmulti-user MIMOOFDMorthogonal frequency division multiplexingOFDMAorthogonal frequency division multiple accessPMIprecoder matrix indicatorRIrank indicatorSISOsingle-input single-outputSU-MIMOsingle user MIMOTxAntransmit antennaUEuser equipmentULuplink
It is to be clearly understood that mere reference herein to previous or existing devices, apparatus, products, systems, methods, practices, publications or to any other information, or to any problems or issues, does not constitute an acknowledgement or admission that any of those things, whether individually or in any combination, formed part of the common general knowledge of those skilled in the field, or that they are admissible prior art.